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提交
abf019f6
编写于
9月 20, 2018
作者:
L
luotao1
浏览文件
操作
浏览文件
下载
差异文件
Merge branch 'develop' into for_test
上级
3ba7e74d
b7588751
变更
8
隐藏空白更改
内联
并排
Showing
8 changed file
with
390 addition
and
420 deletion
+390
-420
doc/README.md
doc/README.md
+7
-0
paddle/fluid/inference/tests/api/analyzer_lac_tester.cc
paddle/fluid/inference/tests/api/analyzer_lac_tester.cc
+57
-91
paddle/fluid/inference/tests/api/analyzer_ner_tester.cc
paddle/fluid/inference/tests/api/analyzer_ner_tester.cc
+56
-80
paddle/fluid/inference/tests/api/analyzer_rnn1_tester.cc
paddle/fluid/inference/tests/api/analyzer_rnn1_tester.cc
+57
-72
paddle/fluid/inference/tests/api/analyzer_rnn2_tester.cc
paddle/fluid/inference/tests/api/analyzer_rnn2_tester.cc
+46
-67
paddle/fluid/inference/tests/api/analyzer_text_classification_tester.cc
...nference/tests/api/analyzer_text_classification_tester.cc
+47
-38
paddle/fluid/inference/tests/api/analyzer_vis_tester.cc
paddle/fluid/inference/tests/api/analyzer_vis_tester.cc
+58
-59
paddle/fluid/inference/tests/api/tester_helper.h
paddle/fluid/inference/tests/api/tester_helper.h
+62
-13
未找到文件。
doc/README.md
0 → 100644
浏览文件 @
abf019f6
# For Readers and Developers
Thanks for reading PaddlePaddle documentation.
Since
**September 17th, 2018**
, the
**0.15.0 and develop**
documentation source has been moved to
[
Fluiddoc Repo
](
https://github.com/PaddlePaddle/Paddle
)
and updated in Fluiddoc Repo.
Please turn to Fluiddoc Repo for the latest documentation.
paddle/fluid/inference/tests/api/analyzer_lac_tester.cc
浏览文件 @
abf019f6
...
...
@@ -103,108 +103,74 @@ void GetOneBatch(std::vector<PaddleTensor> *input_slots, DataRecord *data,
input_slots
->
assign
({
input_tensor
});
}
const
int64_t
lac_ref_data
[]
=
{
24
,
25
,
25
,
25
,
38
,
30
,
31
,
14
,
15
,
44
,
24
,
25
,
25
,
25
,
25
,
25
,
44
,
24
,
25
,
25
,
25
,
36
,
42
,
43
,
44
,
14
,
15
,
44
,
14
,
15
,
44
,
14
,
15
,
44
,
38
,
39
,
14
,
15
,
44
,
22
,
23
,
23
,
23
,
23
,
23
,
23
,
23
};
void
TestLACPrediction
(
const
std
::
string
&
model_path
,
const
std
::
string
&
data_file
,
const
int
batch_size
,
const
int
repeat
,
bool
use_analysis
=
false
)
{
AnalysisConfig
cfg
;
cfg
.
model_dir
=
model_path
;
cfg
.
use_gpu
=
false
;
cfg
.
device
=
0
;
cfg
.
specify_input_name
=
true
;
cfg
.
enable_ir_optim
=
true
;
void
SetConfig
(
AnalysisConfig
*
cfg
)
{
cfg
->
model_dir
=
FLAGS_infer_model
;
cfg
->
use_gpu
=
false
;
cfg
->
device
=
0
;
cfg
->
specify_input_name
=
true
;
cfg
->
enable_ir_optim
=
true
;
}
std
::
vector
<
PaddleTensor
>
input_slots
,
outputs_slots
;
DataRecord
data
(
data_file
,
batch_size
);
GetOneBatch
(
&
input_slots
,
&
data
,
batch_size
);
std
::
unique_ptr
<
PaddlePredictor
>
predictor
;
if
(
use_analysis
)
{
predictor
=
CreatePaddlePredictor
<
AnalysisConfig
,
PaddleEngineKind
::
kAnalysis
>
(
cfg
);
}
else
{
predictor
=
CreatePaddlePredictor
<
NativeConfig
,
PaddleEngineKind
::
kNative
>
(
cfg
);
}
for
(
int
i
=
0
;
i
<
FLAGS_burning
;
i
++
)
{
predictor
->
Run
(
input_slots
,
&
outputs_slots
);
void
SetInput
(
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
*
inputs
)
{
DataRecord
data
(
FLAGS_infer_data
,
FLAGS_batch_size
);
std
::
vector
<
PaddleTensor
>
input_slots
;
int
epoch
=
FLAGS_test_all_data
?
data
.
batched_datas
.
size
()
:
1
;
LOG
(
INFO
)
<<
"number of samples: "
<<
epoch
;
for
(
int
bid
=
0
;
bid
<
epoch
;
++
bid
)
{
GetOneBatch
(
&
input_slots
,
&
data
,
FLAGS_batch_size
);
(
*
inputs
).
emplace_back
(
input_slots
);
}
Timer
timer
;
if
(
FLAGS_test_all_data
)
{
LOG
(
INFO
)
<<
"test all data"
;
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
input_slots_all
;
for
(
size_t
bid
=
0
;
bid
<
data
.
batched_datas
.
size
();
++
bid
)
{
GetOneBatch
(
&
input_slots
,
&
data
,
batch_size
);
input_slots_all
.
emplace_back
(
input_slots
);
}
LOG
(
INFO
)
<<
"total number of samples: "
<<
data
.
datasets
.
size
();
TestPrediction
(
cfg
,
input_slots_all
,
&
outputs_slots
,
FLAGS_num_threads
);
return
;
}
timer
.
tic
();
for
(
int
i
=
0
;
i
<
repeat
;
i
++
)
{
predictor
->
Run
(
input_slots
,
&
outputs_slots
);
}
PrintTime
(
batch_size
,
repeat
,
1
,
0
,
timer
.
toc
()
/
repeat
);
}
// check result
EXPECT_EQ
(
outputs_slots
.
size
(),
1UL
);
auto
&
out
=
outputs_slots
[
0
];
size_t
size
=
std
::
accumulate
(
out
.
shape
.
begin
(),
out
.
shape
.
end
(),
1
,
[](
int
a
,
int
b
)
{
return
a
*
b
;
});
size_t
batch1_size
=
sizeof
(
lac_ref_data
)
/
sizeof
(
int64_t
);
PADDLE_ENFORCE_GT
(
size
,
0
);
EXPECT_GE
(
size
,
batch1_size
);
int64_t
*
pdata
=
static_cast
<
int64_t
*>
(
out
.
data
.
data
());
for
(
size_t
i
=
0
;
i
<
batch1_size
;
++
i
)
{
EXPECT_EQ
(
pdata
[
i
],
lac_ref_data
[
i
]);
}
// Easy for profiling independently.
TEST
(
Analyzer_LAC
,
profile
)
{
AnalysisConfig
cfg
;
SetConfig
(
&
cfg
);
std
::
vector
<
PaddleTensor
>
outputs
;
if
(
use_analysis
)
{
// run once for comparion as reference
auto
ref_predictor
=
CreatePaddlePredictor
<
NativeConfig
,
PaddleEngineKind
::
kNative
>
(
cfg
);
std
::
vector
<
PaddleTensor
>
ref_outputs_slots
;
ref_predictor
->
Run
(
input_slots
,
&
ref_outputs_slots
);
CompareResult
(
ref_outputs_slots
,
outputs_slots
);
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
input_slots_all
;
SetInput
(
&
input_slots_all
);
TestPrediction
(
cfg
,
input_slots_all
,
&
outputs
,
FLAGS_num_threads
);
AnalysisPredictor
*
analysis_predictor
=
dynamic_cast
<
AnalysisPredictor
*>
(
predictor
.
get
());
auto
&
fuse_statis
=
analysis_predictor
->
analysis_argument
()
.
Get
<
std
::
unordered_map
<
std
::
string
,
int
>>
(
framework
::
ir
::
kFuseStatisAttr
);
for
(
auto
&
item
:
fuse_statis
)
{
LOG
(
INFO
)
<<
"fused "
<<
item
.
first
<<
" "
<<
item
.
second
;
}
int
num_ops
=
0
;
for
(
auto
&
node
:
analysis_predictor
->
analysis_argument
().
main_dfg
->
nodes
.
nodes
())
{
if
(
node
->
IsFunction
())
{
++
num_ops
;
}
if
(
FLAGS_num_threads
==
1
&&
!
FLAGS_test_all_data
)
{
// the first inference result
const
int64_t
lac_ref_data
[]
=
{
24
,
25
,
25
,
25
,
38
,
30
,
31
,
14
,
15
,
44
,
24
,
25
,
25
,
25
,
25
,
25
,
44
,
24
,
25
,
25
,
25
,
36
,
42
,
43
,
44
,
14
,
15
,
44
,
14
,
15
,
44
,
14
,
15
,
44
,
38
,
39
,
14
,
15
,
44
,
22
,
23
,
23
,
23
,
23
,
23
,
23
,
23
};
PADDLE_ENFORCE_EQ
(
outputs
.
size
(),
1UL
);
size_t
size
=
GetSize
(
outputs
[
0
]);
size_t
batch1_size
=
sizeof
(
lac_ref_data
)
/
sizeof
(
int64_t
);
PADDLE_ENFORCE_GE
(
size
,
batch1_size
);
int64_t
*
pdata
=
static_cast
<
int64_t
*>
(
outputs
[
0
].
data
.
data
());
for
(
size_t
i
=
0
;
i
<
batch1_size
;
++
i
)
{
EXPECT_EQ
(
pdata
[
i
],
lac_ref_data
[
i
]);
}
LOG
(
INFO
)
<<
"has num ops: "
<<
num_ops
;
ASSERT_TRUE
(
fuse_statis
.
count
(
"fc_fuse"
));
ASSERT_TRUE
(
fuse_statis
.
count
(
"fc_gru_fuse"
));
EXPECT_EQ
(
fuse_statis
.
at
(
"fc_fuse"
),
1
);
EXPECT_EQ
(
fuse_statis
.
at
(
"fc_gru_fuse"
),
4
);
EXPECT_EQ
(
num_ops
,
11
);
}
}
TEST
(
Analyzer_LAC
,
native
)
{
LOG
(
INFO
)
<<
"LAC with native"
;
TestLACPrediction
(
FLAGS_infer_model
,
FLAGS_infer_data
,
FLAGS_batch_size
,
FLAGS_repeat
);
// Check the fuse status
TEST
(
Analyzer_LAC
,
fuse_statis
)
{
AnalysisConfig
cfg
;
SetConfig
(
&
cfg
);
int
num_ops
;
auto
fuse_statis
=
GetFuseStatis
(
cfg
,
&
num_ops
);
ASSERT_TRUE
(
fuse_statis
.
count
(
"fc_fuse"
));
ASSERT_TRUE
(
fuse_statis
.
count
(
"fc_gru_fuse"
));
EXPECT_EQ
(
fuse_statis
.
at
(
"fc_fuse"
),
1
);
EXPECT_EQ
(
fuse_statis
.
at
(
"fc_gru_fuse"
),
4
);
EXPECT_EQ
(
num_ops
,
11
);
}
TEST
(
Analyzer_LAC
,
analysis
)
{
LOG
(
INFO
)
<<
"LAC with analysis"
;
TestLACPrediction
(
FLAGS_infer_model
,
FLAGS_infer_data
,
FLAGS_batch_size
,
FLAGS_repeat
,
true
);
// Compare result of NativeConfig and AnalysisConfig
TEST
(
Analyzer_LAC
,
compare
)
{
AnalysisConfig
cfg
;
SetConfig
(
&
cfg
);
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
input_slots_all
;
SetInput
(
&
input_slots_all
);
CompareNativeAndAnalysis
(
cfg
,
input_slots_all
);
}
}
// namespace analysis
...
...
paddle/fluid/inference/tests/api/analyzer_ner_tester.cc
浏览文件 @
abf019f6
...
...
@@ -95,97 +95,73 @@ void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
}
}
// the first inference result
const
int
chinese_ner_result_data
[]
=
{
30
,
45
,
41
,
48
,
17
,
26
,
48
,
39
,
38
,
16
,
25
};
void
TestChineseNERPrediction
(
bool
use_analysis
)
{
AnalysisConfig
cfg
;
cfg
.
prog_file
=
FLAGS_infer_model
+
"/__model__"
;
cfg
.
param_file
=
FLAGS_infer_model
+
"/param"
;
cfg
.
use_gpu
=
false
;
cfg
.
device
=
0
;
cfg
.
specify_input_name
=
true
;
cfg
.
enable_ir_optim
=
true
;
std
::
vector
<
PaddleTensor
>
input_slots
,
outputs
;
std
::
unique_ptr
<
PaddlePredictor
>
predictor
;
Timer
timer
;
if
(
use_analysis
)
{
predictor
=
CreatePaddlePredictor
<
AnalysisConfig
,
PaddleEngineKind
::
kAnalysis
>
(
cfg
);
}
else
{
predictor
=
CreatePaddlePredictor
<
NativeConfig
,
PaddleEngineKind
::
kNative
>
(
cfg
);
}
void
SetConfig
(
AnalysisConfig
*
cfg
)
{
cfg
->
prog_file
=
FLAGS_infer_model
+
"/__model__"
;
cfg
->
param_file
=
FLAGS_infer_model
+
"/param"
;
cfg
->
use_gpu
=
false
;
cfg
->
device
=
0
;
cfg
->
specify_input_name
=
true
;
cfg
->
enable_ir_optim
=
true
;
}
if
(
FLAGS_test_all_data
)
{
LOG
(
INFO
)
<<
"test all data"
;
DataRecord
data
(
FLAGS_infer_data
,
FLAGS_batch_size
);
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
input_slots_all
;
for
(
size_t
bid
=
0
;
bid
<
data
.
num_samples
/
FLAGS_batch_size
;
++
bid
)
{
PrepareInputs
(
&
input_slots
,
&
data
,
FLAGS_batch_size
);
input_slots_all
.
emplace_back
(
input_slots
);
}
LOG
(
INFO
)
<<
"total number of samples: "
<<
data
.
num_samples
;
TestPrediction
(
cfg
,
input_slots_all
,
&
outputs
,
FLAGS_num_threads
);
return
;
}
// Prepare inputs.
void
SetInput
(
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
*
inputs
)
{
DataRecord
data
(
FLAGS_infer_data
,
FLAGS_batch_size
);
PrepareInputs
(
&
input_slots
,
&
data
,
FLAGS_batch_size
);
timer
.
tic
();
for
(
int
i
=
0
;
i
<
FLAGS_repeat
;
i
++
)
{
predictor
->
Run
(
input_slots
,
&
outputs
);
std
::
vector
<
PaddleTensor
>
input_slots
;
int
epoch
=
FLAGS_test_all_data
?
data
.
num_samples
/
FLAGS_batch_size
:
1
;
LOG
(
INFO
)
<<
"number of samples: "
<<
epoch
*
FLAGS_batch_size
;
for
(
int
bid
=
0
;
bid
<
epoch
;
++
bid
)
{
PrepareInputs
(
&
input_slots
,
&
data
,
FLAGS_batch_size
);
(
*
inputs
).
emplace_back
(
input_slots
);
}
PrintTime
(
FLAGS_batch_size
,
FLAGS_repeat
,
1
,
0
,
timer
.
toc
()
/
FLAGS_repeat
);
}
PADDLE_ENFORCE
(
outputs
.
size
(),
1UL
);
auto
&
out
=
outputs
[
0
];
size_t
size
=
std
::
accumulate
(
out
.
shape
.
begin
(),
out
.
shape
.
end
(),
1
,
[](
int
a
,
int
b
)
{
return
a
*
b
;
});
PADDLE_ENFORCE_GT
(
size
,
0
);
int64_t
*
result
=
static_cast
<
int64_t
*>
(
out
.
data
.
data
());
for
(
size_t
i
=
0
;
i
<
std
::
min
(
11UL
,
size
);
i
++
)
{
PADDLE_ENFORCE
(
result
[
i
],
chinese_ner_result_data
[
i
]);
}
// Easy for profiling independently.
TEST
(
Analyzer_Chinese_ner
,
profile
)
{
AnalysisConfig
cfg
;
SetConfig
(
&
cfg
);
std
::
vector
<
PaddleTensor
>
outputs
;
if
(
use_analysis
)
{
// run once for comparion as reference
auto
ref_predictor
=
CreatePaddlePredictor
<
NativeConfig
,
PaddleEngineKind
::
kNative
>
(
cfg
);
std
::
vector
<
PaddleTensor
>
ref_outputs_slots
;
ref_predictor
->
Run
(
input_slots
,
&
ref_outputs_slots
);
CompareResult
(
ref_outputs_slots
,
outputs
);
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
input_slots_all
;
SetInput
(
&
input_slots_all
);
TestPrediction
(
cfg
,
input_slots_all
,
&
outputs
,
FLAGS_num_threads
);
AnalysisPredictor
*
analysis_predictor
=
dynamic_cast
<
AnalysisPredictor
*>
(
predictor
.
get
());
auto
&
fuse_statis
=
analysis_predictor
->
analysis_argument
()
.
Get
<
std
::
unordered_map
<
std
::
string
,
int
>>
(
framework
::
ir
::
kFuseStatisAttr
);
for
(
auto
&
item
:
fuse_statis
)
{
LOG
(
INFO
)
<<
"fused "
<<
item
.
first
<<
" "
<<
item
.
second
;
}
int
num_ops
=
0
;
for
(
auto
&
node
:
analysis_predictor
->
analysis_argument
().
main_dfg
->
nodes
.
nodes
())
{
if
(
node
->
IsFunction
())
{
++
num_ops
;
}
if
(
FLAGS_num_threads
==
1
&&
!
FLAGS_test_all_data
)
{
// the first inference result
const
int
chinese_ner_result_data
[]
=
{
30
,
45
,
41
,
48
,
17
,
26
,
48
,
39
,
38
,
16
,
25
};
PADDLE_ENFORCE_EQ
(
outputs
.
size
(),
1UL
);
size_t
size
=
GetSize
(
outputs
[
0
]);
PADDLE_ENFORCE_GT
(
size
,
0
);
int64_t
*
result
=
static_cast
<
int64_t
*>
(
outputs
[
0
].
data
.
data
());
for
(
size_t
i
=
0
;
i
<
std
::
min
(
11UL
,
size
);
i
++
)
{
EXPECT_EQ
(
result
[
i
],
chinese_ner_result_data
[
i
]);
}
LOG
(
INFO
)
<<
"has num ops: "
<<
num_ops
;
ASSERT_TRUE
(
fuse_statis
.
count
(
"fc_fuse"
));
ASSERT_TRUE
(
fuse_statis
.
count
(
"fc_gru_fuse"
));
EXPECT_EQ
(
fuse_statis
.
at
(
"fc_fuse"
),
1
);
EXPECT_EQ
(
fuse_statis
.
at
(
"fc_gru_fuse"
),
2
);
EXPECT_EQ
(
num_ops
,
14
);
}
}
TEST
(
Analyzer_Chinese_ner
,
native
)
{
TestChineseNERPrediction
(
false
);
}
// Check the fuse status
TEST
(
Analyzer_Chinese_ner
,
fuse_statis
)
{
AnalysisConfig
cfg
;
SetConfig
(
&
cfg
);
TEST
(
Analyzer_Chinese_ner
,
analysis
)
{
TestChineseNERPrediction
(
true
);
}
int
num_ops
;
auto
fuse_statis
=
GetFuseStatis
(
cfg
,
&
num_ops
);
ASSERT_TRUE
(
fuse_statis
.
count
(
"fc_fuse"
));
ASSERT_TRUE
(
fuse_statis
.
count
(
"fc_gru_fuse"
));
EXPECT_EQ
(
fuse_statis
.
at
(
"fc_fuse"
),
1
);
EXPECT_EQ
(
fuse_statis
.
at
(
"fc_gru_fuse"
),
2
);
EXPECT_EQ
(
num_ops
,
14
);
}
// Compare result of NativeConfig and AnalysisConfig
TEST
(
Analyzer_Chinese_ner
,
compare
)
{
AnalysisConfig
cfg
;
SetConfig
(
&
cfg
);
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
input_slots_all
;
SetInput
(
&
input_slots_all
);
CompareNativeAndAnalysis
(
cfg
,
input_slots_all
);
}
}
// namespace inference
}
// namespace paddle
paddle/fluid/inference/tests/api/analyzer_rnn1_tester.cc
浏览文件 @
abf019f6
...
...
@@ -25,6 +25,7 @@ struct DataRecord {
std
::
vector
<
size_t
>
lod1
,
lod2
,
lod3
;
std
::
vector
<
std
::
vector
<
float
>>
rnn_link_data
,
rnn_week_datas
,
rnn_minute_datas
;
size_t
num_samples
;
// total number of samples
size_t
batch_iter
{
0
};
size_t
batch_size
{
1
};
DataRecord
()
=
default
;
...
...
@@ -97,6 +98,7 @@ struct DataRecord {
week_data_all
.
push_back
(
std
::
move
(
week_data
));
minute_data_all
.
push_back
(
std
::
move
(
minute_data
));
}
num_samples
=
num_lines
;
}
};
void
PrepareInputs
(
std
::
vector
<
PaddleTensor
>
*
input_slots
,
DataRecord
*
data
,
...
...
@@ -147,89 +149,72 @@ void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
}
}
// Test with a really complicate model.
void
TestRNN1Prediction
(
bool
use_analysis
,
bool
activate_ir
,
int
num_threads
)
{
AnalysisConfig
config
;
config
.
prog_file
=
FLAGS_infer_model
+
"/__model__"
;
config
.
param_file
=
FLAGS_infer_model
+
"/param"
;
config
.
use_gpu
=
false
;
config
.
device
=
0
;
config
.
specify_input_name
=
true
;
config
.
enable_ir_optim
=
activate_ir
;
PADDLE_ENFORCE
(
config
.
ir_mode
==
AnalysisConfig
::
IrPassMode
::
kExclude
);
// default
config
.
ir_passes
.
clear
();
// Do not exclude any pass.
int
batch_size
=
FLAGS_batch_size
;
void
SetConfig
(
AnalysisConfig
*
cfg
)
{
cfg
->
prog_file
=
FLAGS_infer_model
+
"/__model__"
;
cfg
->
param_file
=
FLAGS_infer_model
+
"/param"
;
cfg
->
use_gpu
=
false
;
cfg
->
device
=
0
;
cfg
->
specify_input_name
=
true
;
cfg
->
enable_ir_optim
=
true
;
cfg
->
ir_passes
.
clear
();
// Do not exclude any pass.
}
auto
base_predictor
=
CreatePaddlePredictor
<
NativeConfig
,
PaddleEngineKind
::
kNative
>
(
config
);
auto
predictor
=
CreatePaddlePredictor
<
AnalysisConfig
,
PaddleEngineKind
::
kAnalysis
>
(
config
);
void
SetInput
(
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
*
inputs
)
{
DataRecord
data
(
FLAGS_infer_data
,
FLAGS_batch_size
);
std
::
vector
<
PaddleTensor
>
input_slots
;
DataRecord
data
(
FLAGS_infer_data
,
batch_size
);
// Prepare inputs.
PrepareInputs
(
&
input_slots
,
&
data
,
batch_size
);
std
::
vector
<
PaddleTensor
>
outputs
,
base_outputs
;
int
epoch
=
FLAGS_test_all_data
?
data
.
num_samples
/
FLAGS_batch_size
:
1
;
LOG
(
INFO
)
<<
"number of samples: "
<<
epoch
*
FLAGS_batch_size
;
for
(
int
bid
=
0
;
bid
<
epoch
;
++
bid
)
{
PrepareInputs
(
&
input_slots
,
&
data
,
FLAGS_batch_size
);
(
*
inputs
).
emplace_back
(
input_slots
);
}
}
base_predictor
->
Run
(
input_slots
,
&
base_outputs
);
// Easy for profiling independently.
TEST
(
Analyzer_rnn1
,
profile
)
{
AnalysisConfig
cfg
;
SetConfig
(
&
cfg
);
std
::
vector
<
PaddleTensor
>
outputs
;
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
input_slots_all
;
input_slots_all
.
emplace_back
(
input_slots
);
if
(
num_threads
==
1
)
{
TestOneThreadPrediction
(
config
,
input_slots_all
,
&
outputs
);
CompareResult
(
outputs
,
base_outputs
);
}
else
{
// only return the output of first thread
TestMultiThreadPrediction
(
config
,
input_slots_all
,
&
outputs
,
num_threads
);
}
SetInput
(
&
input_slots_all
);
TestPrediction
(
cfg
,
input_slots_all
,
&
outputs
,
FLAGS_num_threads
);
}
if
(
use_analysis
&&
activate_ir
)
{
AnalysisPredictor
*
analysis_predictor
=
dynamic_cast
<
AnalysisPredictor
*>
(
predictor
.
get
());
auto
&
fuse_statis
=
analysis_predictor
->
analysis_argument
()
.
Get
<
std
::
unordered_map
<
std
::
string
,
int
>>
(
framework
::
ir
::
kFuseStatisAttr
);
for
(
auto
&
item
:
fuse_statis
)
{
LOG
(
INFO
)
<<
"fused "
<<
item
.
first
<<
" "
<<
item
.
second
;
}
// Check the fuse status
TEST
(
Analyzer_rnn1
,
fuse_statis
)
{
AnalysisConfig
cfg
;
SetConfig
(
&
cfg
);
int
num_ops
=
0
;
for
(
auto
&
node
:
analysis_predictor
->
analysis_argument
().
main_dfg
->
nodes
.
nodes
())
{
if
(
node
->
IsFunction
())
{
++
num_ops
;
}
}
LOG
(
INFO
)
<<
"has num ops: "
<<
num_ops
;
int
num_ops
;
auto
fuse_statis
=
GetFuseStatis
(
cfg
,
&
num_ops
);
ASSERT_TRUE
(
fuse_statis
.
count
(
"fc_fuse"
));
EXPECT_EQ
(
fuse_statis
.
at
(
"fc_fuse"
),
1
);
EXPECT_EQ
(
fuse_statis
.
at
(
"fc_nobias_lstm_fuse"
),
2
);
// bi-directional LSTM
EXPECT_EQ
(
fuse_statis
.
at
(
"seq_concat_fc_fuse"
),
1
);
EXPECT_EQ
(
num_ops
,
13
);
// After graph optimization, only 13 operators exists.
}
ASSERT_TRUE
(
fuse_statis
.
count
(
"fc_fuse"
));
EXPECT_EQ
(
fuse_statis
.
at
(
"fc_fuse"
),
1
);
EXPECT_EQ
(
fuse_statis
.
at
(
"fc_nobias_lstm_fuse"
),
2
);
// bi-directional LSTM
EXPECT_EQ
(
fuse_statis
.
at
(
"seq_concat_fc_fuse"
),
1
);
EXPECT_EQ
(
num_ops
,
13
);
// After graph optimization, only 13 operators exists.
}
// Compare result of NativeConfig and AnalysisConfig
TEST
(
Analyzer_rnn1
,
compare
)
{
AnalysisConfig
cfg
;
SetConfig
(
&
cfg
);
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
input_slots_all
;
SetInput
(
&
input_slots_all
);
CompareNativeAndAnalysis
(
cfg
,
input_slots_all
);
}
// Inference with analysis and IR, easy for profiling independently.
TEST
(
Analyzer
,
rnn1
)
{
TestRNN1Prediction
(
true
,
true
,
FLAGS_num_threads
);
}
// Test Multi-Thread.
TEST
(
Analyzer_rnn1
,
multi_thread
)
{
AnalysisConfig
cfg
;
SetConfig
(
&
cfg
);
std
::
vector
<
PaddleTensor
>
outputs
;
// Other unit-tests of RNN1, test different options of use_analysis,
// activate_ir and multi-threads.
TEST
(
Analyzer
,
RNN_tests
)
{
int
num_threads
[
2
]
=
{
1
,
4
};
for
(
auto
i
:
num_threads
)
{
// Directly infer with the original model.
TestRNN1Prediction
(
false
,
false
,
i
);
// Inference with the original model with the analysis turned on, the
// analysis module will transform the program to a data flow graph.
TestRNN1Prediction
(
true
,
false
,
i
);
// Inference with analysis and IR. The IR module will fuse some large
// kernels.
TestRNN1Prediction
(
true
,
true
,
i
);
}
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
input_slots_all
;
SetInput
(
&
input_slots_all
);
TestPrediction
(
cfg
,
input_slots_all
,
&
outputs
,
4
/* num_threads */
);
}
}
// namespace inference
...
...
paddle/fluid/inference/tests/api/analyzer_rnn2_tester.cc
浏览文件 @
abf019f6
...
...
@@ -12,24 +12,7 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/inference/analysis/analyzer.h"
#include <google/protobuf/text_format.h>
#include <gtest/gtest.h>
#include <thread> // NOLINT
#include "paddle/fluid/framework/ir/fuse_pass_base.h"
#include "paddle/fluid/framework/ir/pass.h"
#include "paddle/fluid/inference/analysis/ut_helper.h"
#include "paddle/fluid/inference/api/analysis_predictor.h"
#include "paddle/fluid/inference/api/helper.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
#include "paddle/fluid/inference/api/paddle_inference_pass.h"
DEFINE_string
(
infer_model
,
""
,
"model path"
);
DEFINE_string
(
infer_data
,
""
,
"data path"
);
DEFINE_int32
(
batch_size
,
1
,
"batch size."
);
DEFINE_int32
(
repeat
,
1
,
"Running the inference program repeat times."
);
DEFINE_int32
(
num_threads
,
1
,
"Running the inference program in multi-threads."
);
#include "paddle/fluid/inference/tests/api/tester_helper.h"
namespace
paddle
{
namespace
inference
{
...
...
@@ -41,6 +24,7 @@ struct DataRecord {
std
::
vector
<
size_t
>
lod
;
std
::
vector
<
std
::
vector
<
float
>>
rnn_link_data
;
std
::
vector
<
float
>
result_data
;
size_t
num_samples
;
// total number of samples
size_t
batch_iter
{
0
};
size_t
batch_size
{
1
};
DataRecord
()
=
default
;
...
...
@@ -100,6 +84,7 @@ struct DataRecord {
result_data
.
insert
(
result_data
.
end
(),
tmp
.
begin
(),
tmp
.
end
());
}
}
num_samples
=
num_lines
/
2
;
}
};
void
PrepareInputs
(
std
::
vector
<
PaddleTensor
>
*
input_slots
,
DataRecord
*
data
,
...
...
@@ -118,64 +103,58 @@ void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
input_slots
->
assign
({
feed_tensor
});
}
void
CompareResult
(
const
std
::
vector
<
PaddleTensor
>
&
outputs
,
const
std
::
vector
<
float
>
&
base_result
)
{
PADDLE_ENFORCE_GT
(
outputs
.
size
(),
0
);
for
(
size_t
i
=
0
;
i
<
outputs
.
size
();
i
++
)
{
auto
&
out
=
outputs
[
i
];
size_t
size
=
std
::
accumulate
(
out
.
shape
.
begin
(),
out
.
shape
.
end
(),
1
,
[](
int
a
,
int
b
)
{
return
a
*
b
;
});
PADDLE_ENFORCE_GT
(
size
,
0
);
float
*
data
=
static_cast
<
float
*>
(
out
.
data
.
data
());
for
(
size_t
i
=
0
;
i
<
size
;
i
++
)
{
EXPECT_NEAR
(
data
[
i
],
base_result
[
i
],
1e-3
);
}
void
SetConfig
(
AnalysisConfig
*
cfg
)
{
cfg
->
prog_file
=
FLAGS_infer_model
+
"/__model__"
;
cfg
->
param_file
=
FLAGS_infer_model
+
"/param"
;
cfg
->
use_gpu
=
false
;
cfg
->
device
=
0
;
cfg
->
specify_input_name
=
true
;
cfg
->
enable_ir_optim
=
true
;
}
void
SetInput
(
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
*
inputs
)
{
DataRecord
data
(
FLAGS_infer_data
,
FLAGS_batch_size
);
std
::
vector
<
PaddleTensor
>
input_slots
;
int
epoch
=
FLAGS_test_all_data
?
data
.
num_samples
/
FLAGS_batch_size
:
1
;
LOG
(
INFO
)
<<
"number of samples: "
<<
epoch
*
FLAGS_batch_size
;
for
(
int
bid
=
0
;
bid
<
epoch
;
++
bid
)
{
PrepareInputs
(
&
input_slots
,
&
data
,
FLAGS_batch_size
);
(
*
inputs
).
emplace_back
(
input_slots
);
}
}
// Test with a really complicate model.
void
TestRNN2Prediction
()
{
AnalysisConfig
config
;
config
.
prog_file
=
FLAGS_infer_model
+
"/__model__"
;
config
.
param_file
=
FLAGS_infer_model
+
"/param"
;
config
.
use_gpu
=
false
;
config
.
device
=
0
;
config
.
specify_input_name
=
true
;
config
.
enable_ir_optim
=
true
;
PADDLE_ENFORCE
(
config
.
ir_mode
==
AnalysisConfig
::
IrPassMode
::
kExclude
);
// default
int
batch_size
=
FLAGS_batch_size
;
int
num_times
=
FLAGS_repeat
;
// Easy for profiling independently.
TEST
(
Analyzer_rnn2
,
profile
)
{
AnalysisConfig
cfg
;
SetConfig
(
&
cfg
);
std
::
vector
<
PaddleTensor
>
outputs
;
auto
base_predictor
=
CreatePaddlePredictor
<
NativeConfig
,
PaddleEngineKind
::
kNative
>
(
config
);
auto
predictor
=
CreatePaddlePredictor
<
AnalysisConfig
,
PaddleEngineKind
::
kAnalysis
>
(
config
);
std
::
vector
<
PaddleTensor
>
input_slots
;
DataRecord
data
(
FLAGS_infer_data
,
batch_size
);
PrepareInputs
(
&
input_slots
,
&
data
,
batch_size
);
std
::
vector
<
PaddleTensor
>
outputs
,
base_outputs
;
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
input_slots_all
;
SetInput
(
&
input_slots_all
);
TestPrediction
(
cfg
,
input_slots_all
,
&
outputs
,
FLAGS_num_threads
);
Timer
timer1
;
timer1
.
tic
();
for
(
int
i
=
0
;
i
<
num_times
;
i
++
)
{
base_predictor
->
Run
(
input_slots
,
&
base_outputs
);
if
(
FLAGS_num_threads
==
1
&&
!
FLAGS_test_all_data
)
{
// the first inference result
DataRecord
data
(
FLAGS_infer_data
,
FLAGS_batch_size
);
PADDLE_ENFORCE_GT
(
outputs
.
size
(),
0
);
size_t
size
=
GetSize
(
outputs
[
0
]);
PADDLE_ENFORCE_GT
(
size
,
0
);
float
*
result
=
static_cast
<
float
*>
(
outputs
[
0
].
data
.
data
());
for
(
size_t
i
=
0
;
i
<
size
;
i
++
)
{
EXPECT_NEAR
(
result
[
i
],
data
.
result_data
[
i
],
1e-3
);
}
}
PrintTime
(
batch_size
,
num_times
,
1
,
0
,
timer1
.
toc
()
/
num_times
);
}
Timer
timer2
;
timer2
.
tic
();
for
(
int
i
=
0
;
i
<
num_times
;
i
++
)
{
predictor
->
Run
(
input_slots
,
&
outputs
);
}
PrintTime
(
batch_size
,
num_times
,
1
,
0
,
timer2
.
toc
()
/
num_times
);
// Compare result of NativeConfig and AnalysisConfig
TEST
(
Analyzer_rnn2
,
compare
)
{
AnalysisConfig
cfg
;
SetConfig
(
&
cfg
);
CompareResult
(
base_outputs
,
data
.
result_data
);
CompareResult
(
outputs
,
data
.
result_data
);
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
input_slots_all
;
SetInput
(
&
input_slots_all
);
CompareNativeAndAnalysis
(
cfg
,
input_slots_all
);
}
TEST
(
Analyzer
,
rnn2
)
{
TestRNN2Prediction
();
}
}
// namespace inference
}
// namespace paddle
paddle/fluid/inference/tests/api/analyzer_text_classification_tester.cc
浏览文件 @
abf019f6
...
...
@@ -46,54 +46,63 @@ struct DataReader {
std
::
unique_ptr
<
std
::
ifstream
>
file
;
};
void
Main
(
int
batch_size
)
{
// shape --
// Create Predictor --
AnalysisConfig
config
;
c
onfig
.
model_dir
=
FLAGS_infer_model
;
c
onfig
.
use_gpu
=
fals
e
;
config
.
enable_ir_optim
=
true
;
void
SetConfig
(
AnalysisConfig
*
cfg
)
{
cfg
->
model_dir
=
FLAGS_infer_model
;
cfg
->
use_gpu
=
false
;
cfg
->
device
=
0
;
c
fg
->
specify_input_name
=
true
;
c
fg
->
enable_ir_optim
=
tru
e
;
}
std
::
vector
<
PaddleTensor
>
input_slots
,
output_slots
;
void
SetInput
(
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
*
inputs
)
{
std
::
vector
<
PaddleTensor
>
input_slots
;
DataReader
reader
(
FLAGS_infer_data
);
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
input_slots_all
;
if
(
FLAGS_test_all_data
)
{
LOG
(
INFO
)
<<
"test all data"
;
int
num_batches
=
0
;
while
(
reader
.
NextBatch
(
&
input_slots
,
FLAGS_batch_size
))
{
input_slots_all
.
emplace_back
(
input_slots
);
++
num_batches
;
}
LOG
(
INFO
)
<<
"total number of samples: "
<<
num_batches
*
FLAGS_batch_size
;
TestPrediction
(
config
,
input_slots_all
,
&
output_slots
,
FLAGS_num_threads
);
return
;
int
num_batches
=
0
;
while
(
reader
.
NextBatch
(
&
input_slots
,
FLAGS_batch_size
))
{
(
*
inputs
).
emplace_back
(
input_slots
);
++
num_batches
;
if
(
!
FLAGS_test_all_data
)
return
;
}
LOG
(
INFO
)
<<
"total number of samples: "
<<
num_batches
*
FLAGS_batch_size
;
}
// one batch starts
// data --
reader
.
NextBatch
(
&
input_slots
,
FLAGS_batch_size
)
;
input_slots_all
.
emplace_back
(
input_slots
);
TestPrediction
(
config
,
input_slots_all
,
&
output_slots
,
FLAGS_num_threads
)
;
// Easy for profiling independently.
TEST
(
Analyzer_Text_Classification
,
profile
)
{
AnalysisConfig
cfg
;
SetConfig
(
&
cfg
);
std
::
vector
<
PaddleTensor
>
outputs
;
// Get output
LOG
(
INFO
)
<<
"get outputs "
<<
output_slots
.
size
();
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
input_slots_all
;
SetInput
(
&
input_slots_all
);
TestPrediction
(
cfg
,
input_slots_all
,
&
outputs
,
FLAGS_num_threads
);
for
(
auto
&
output
:
output_slots
)
{
LOG
(
INFO
)
<<
"output.shape: "
<<
to_string
(
output
.
shape
);
// no lod ?
CHECK_EQ
(
output
.
lod
.
size
(),
0UL
);
LOG
(
INFO
)
<<
"output.dtype: "
<<
output
.
dtype
;
std
::
stringstream
ss
;
for
(
int
i
=
0
;
i
<
5
;
i
++
)
{
ss
<<
static_cast
<
float
*>
(
output
.
data
.
data
())[
i
]
<<
" "
;
if
(
FLAGS_num_threads
==
1
)
{
// Get output
LOG
(
INFO
)
<<
"get outputs "
<<
outputs
.
size
();
for
(
auto
&
output
:
outputs
)
{
LOG
(
INFO
)
<<
"output.shape: "
<<
to_string
(
output
.
shape
);
// no lod ?
CHECK_EQ
(
output
.
lod
.
size
(),
0UL
);
LOG
(
INFO
)
<<
"output.dtype: "
<<
output
.
dtype
;
std
::
stringstream
ss
;
for
(
int
i
=
0
;
i
<
5
;
i
++
)
{
ss
<<
static_cast
<
float
*>
(
output
.
data
.
data
())[
i
]
<<
" "
;
}
LOG
(
INFO
)
<<
"output.data summary: "
<<
ss
.
str
();
// one batch ends
}
LOG
(
INFO
)
<<
"output.data summary: "
<<
ss
.
str
();
// one batch ends
}
}
TEST
(
text_classification
,
basic
)
{
Main
(
FLAGS_batch_size
);
}
// Compare result of NativeConfig and AnalysisConfig
TEST
(
Analyzer_Text_Classification
,
compare
)
{
AnalysisConfig
cfg
;
SetConfig
(
&
cfg
);
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
input_slots_all
;
SetInput
(
&
input_slots_all
);
CompareNativeAndAnalysis
(
cfg
,
input_slots_all
);
}
}
// namespace inference
}
// namespace paddle
paddle/fluid/inference/tests/api/analyzer_vis_tester.cc
浏览文件 @
abf019f6
...
...
@@ -49,84 +49,83 @@ Record ProcessALine(const std::string &line) {
return
record
;
}
/*
* Use the native and analysis fluid engine to inference the demo.
* ocr, mobilenet and se_resnext50
*/
void
TestVisualPrediction
(
bool
use_mkldnn
)
{
std
::
unique_ptr
<
PaddlePredictor
>
predictor
;
AnalysisConfig
cfg
;
cfg
.
param_file
=
FLAGS_infer_model
+
"/__params__"
;
cfg
.
prog_file
=
FLAGS_infer_model
+
"/__model__"
;
cfg
.
use_gpu
=
false
;
cfg
.
_use_mkldnn
=
use_mkldnn
;
cfg
.
device
=
0
;
cfg
.
enable_ir_optim
=
true
;
void
SetConfig
(
AnalysisConfig
*
cfg
)
{
cfg
->
param_file
=
FLAGS_infer_model
+
"/__params__"
;
cfg
->
prog_file
=
FLAGS_infer_model
+
"/__model__"
;
cfg
->
use_gpu
=
false
;
cfg
->
device
=
0
;
cfg
->
enable_ir_optim
=
true
;
cfg
->
specify_input_name
=
true
;
// TODO(TJ): fix fusion gru
cfg
.
ir_passes
.
push_back
(
"fc_gru_fuse_pass"
);
cfg
->
ir_passes
.
push_back
(
"fc_gru_fuse_pass"
);
#ifdef PADDLE_WITH_MKLDNN
cfg
->
_use_mkldnn
=
true
;
// disable mkldnn fuse since it should have some bugs
cfg
.
ir_passes
.
push_back
(
"conv_relu_mkldnn_fuse_pass"
);
cfg
->
ir_passes
.
push_back
(
"conv_relu_mkldnn_fuse_pass"
);
#endif
predictor
=
CreatePaddlePredictor
<
AnalysisConfig
,
PaddleEngineKind
::
kAnalysis
>
(
cfg
);
}
// Only have single batch of data.
void
SetInput
(
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
*
inputs
)
{
PADDLE_ENFORCE_EQ
(
FLAGS_test_all_data
,
0
,
"Only have single batch of data."
);
std
::
string
line
;
std
::
ifstream
file
(
FLAGS_infer_data
);
std
::
getline
(
file
,
line
);
auto
record
=
ProcessALine
(
line
);
file
.
close
();
// Inference.
PaddleTensor
input
;
input
.
shape
=
record
.
shape
;
input
.
data
=
PaddleBuf
(
record
.
data
.
data
(),
record
.
data
.
size
()
*
sizeof
(
float
));
input
.
dtype
=
PaddleDType
::
FLOAT32
;
size_t
input_size
=
record
.
data
.
size
()
*
sizeof
(
float
);
input
.
data
.
Resize
(
input_size
);
memcpy
(
input
.
data
.
data
(),
record
.
data
.
data
(),
input_size
);
std
::
vector
<
PaddleTensor
>
input_slots
;
input_slots
.
assign
({
input
});
(
*
inputs
).
emplace_back
(
input_slots
);
}
std
::
vector
<
PaddleTensor
>
outputs_slots
;
Timer
timer
;
timer
.
tic
();
for
(
int
i
=
0
;
i
<
FLAGS_repeat
;
i
++
)
{
predictor
->
Run
({
input
},
&
outputs_slots
);
}
PrintTime
(
/*batch size*/
1
,
FLAGS_repeat
,
/*num threads*/
1
,
/*thread id*/
0
,
timer
.
toc
()
/
FLAGS_repeat
);
VLOG
(
3
)
<<
"output.size "
<<
outputs_slots
.
size
();
// run native as reference
auto
ref_predictor
=
CreatePaddlePredictor
<
NativeConfig
,
PaddleEngineKind
::
kNative
>
(
cfg
);
std
::
vector
<
PaddleTensor
>
ref_outputs_slots
;
ref_predictor
->
Run
({
input
},
&
ref_outputs_slots
);
CompareResult
(
outputs_slots
,
ref_outputs_slots
);
// print what are fused
AnalysisPredictor
*
analysis_predictor
=
dynamic_cast
<
AnalysisPredictor
*>
(
predictor
.
get
());
auto
&
fuse_statis
=
analysis_predictor
->
analysis_argument
()
.
Get
<
std
::
unordered_map
<
std
::
string
,
int
>>
(
framework
::
ir
::
kFuseStatisAttr
);
for
(
auto
&
item
:
fuse_statis
)
{
LOG
(
INFO
)
<<
"fused "
<<
item
.
first
<<
" "
<<
item
.
second
;
}
int
num_ops
=
0
;
for
(
auto
&
node
:
analysis_predictor
->
analysis_argument
().
main_dfg
->
nodes
.
nodes
())
{
if
(
node
->
IsFunction
())
{
++
num_ops
;
// Easy for profiling independently.
// ocr, mobilenet and se_resnext50
TEST
(
Analyzer_vis
,
profile
)
{
AnalysisConfig
cfg
;
SetConfig
(
&
cfg
);
std
::
vector
<
PaddleTensor
>
outputs
;
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
input_slots_all
;
SetInput
(
&
input_slots_all
);
TestPrediction
(
cfg
,
input_slots_all
,
&
outputs
,
FLAGS_num_threads
);
if
(
FLAGS_num_threads
==
1
&&
!
FLAGS_test_all_data
)
{
const
float
ocr_result_data
[]
=
{
5.273636460856323538e-08
,
3.296741795111302054e-07
,
1.873261190610264748e-08
,
3.403730275408634043e-08
,
3.383312474625199684e-08
};
PADDLE_ENFORCE_EQ
(
outputs
.
size
(),
1UL
);
size_t
size
=
GetSize
(
outputs
[
0
]);
PADDLE_ENFORCE_GT
(
size
,
0
);
float
*
result
=
static_cast
<
float
*>
(
outputs
[
0
].
data
.
data
());
for
(
size_t
i
=
0
;
i
<
std
::
min
(
5UL
,
size
);
i
++
)
{
EXPECT_NEAR
(
result
[
i
],
ocr_result_data
[
i
],
1e-3
);
}
}
LOG
(
INFO
)
<<
"has num ops: "
<<
num_ops
;
}
TEST
(
Analyzer_vis
,
analysis
)
{
TestVisualPrediction
(
/*use_mkldnn*/
false
);
}
#ifdef PADDLE_WITH_MKLDNN
TEST
(
Analyzer_vis
,
analysis_mkldnn
)
{
TestVisualPrediction
(
/*use_mkldnn*/
true
);
// Check the fuse status
TEST
(
Analyzer_vis
,
fuse_statis
)
{
AnalysisConfig
cfg
;
SetConfig
(
&
cfg
);
int
num_ops
;
GetFuseStatis
(
cfg
,
&
num_ops
);
}
// Compare result of NativeConfig and AnalysisConfig
TEST
(
Analyzer_vis
,
compare
)
{
AnalysisConfig
cfg
;
SetConfig
(
&
cfg
);
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
input_slots_all
;
SetInput
(
&
input_slots_all
);
CompareNativeAndAnalysis
(
cfg
,
input_slots_all
);
}
#endif
}
// namespace analysis
}
// namespace inference
...
...
paddle/fluid/inference/tests/api/tester_helper.h
浏览文件 @
abf019f6
...
...
@@ -15,6 +15,7 @@
#pragma once
#include <gtest/gtest.h>
#include <string>
#include <thread> // NOLINT
#include <vector>
#include "paddle/fluid/framework/ir/fuse_pass_base.h"
...
...
@@ -28,17 +29,18 @@
DEFINE_string
(
infer_model
,
""
,
"model path"
);
DEFINE_string
(
infer_data
,
""
,
"data file"
);
DEFINE_int32
(
batch_size
,
1
,
"batch size."
);
DEFINE_int32
(
burning
,
0
,
"Burning before repeat."
);
DEFINE_int32
(
repeat
,
1
,
"Running the inference program repeat times."
);
DEFINE_bool
(
test_all_data
,
false
,
"Test the all dataset in data file."
);
DEFINE_int32
(
num_threads
,
1
,
"Running the inference program in multi-threads."
);
DEFINE_bool
(
use_analysis
,
true
,
"Running the inference program in analysis mode."
);
namespace
paddle
{
namespace
inference
{
void
CompareResult
(
const
std
::
vector
<
PaddleTensor
>
&
outputs
,
const
std
::
vector
<
PaddleTensor
>
&
ref_outputs
)
{
EXPECT_GT
(
outputs
.
size
(),
0
);
EXPECT_GT
(
outputs
.
size
(),
0
UL
);
EXPECT_EQ
(
outputs
.
size
(),
ref_outputs
.
size
());
for
(
size_t
i
=
0
;
i
<
outputs
.
size
();
i
++
)
{
auto
&
out
=
outputs
[
i
];
...
...
@@ -72,14 +74,50 @@ void CompareResult(const std::vector<PaddleTensor> &outputs,
}
}
std
::
unique_ptr
<
PaddlePredictor
>
GetPrediction
(
AnalysisConfig
config
,
bool
use_analysis
=
true
)
{
if
(
use_analysis
)
{
return
CreatePaddlePredictor
<
AnalysisConfig
,
PaddleEngineKind
::
kAnalysis
>
(
config
);
}
else
{
return
CreatePaddlePredictor
<
NativeConfig
,
PaddleEngineKind
::
kNative
>
(
config
);
}
}
size_t
GetSize
(
const
PaddleTensor
&
out
)
{
return
std
::
accumulate
(
out
.
shape
.
begin
(),
out
.
shape
.
end
(),
1
,
[](
int
a
,
int
b
)
{
return
a
*
b
;
});
}
std
::
unordered_map
<
std
::
string
,
int
>
GetFuseStatis
(
AnalysisConfig
config
,
int
*
num_ops
)
{
auto
predictor
=
GetPrediction
(
config
);
AnalysisPredictor
*
analysis_predictor
=
dynamic_cast
<
AnalysisPredictor
*>
(
predictor
.
get
());
auto
&
fuse_statis
=
analysis_predictor
->
analysis_argument
()
.
Get
<
std
::
unordered_map
<
std
::
string
,
int
>>
(
framework
::
ir
::
kFuseStatisAttr
);
for
(
auto
&
item
:
fuse_statis
)
{
LOG
(
INFO
)
<<
"fused "
<<
item
.
first
<<
" "
<<
item
.
second
;
}
int
num
=
0
;
for
(
auto
&
node
:
analysis_predictor
->
analysis_argument
().
main_dfg
->
nodes
.
nodes
())
{
if
(
node
->
IsFunction
())
{
++
num
;
}
}
*
num_ops
=
num
;
return
fuse_statis
;
}
void
TestOneThreadPrediction
(
AnalysisConfig
config
,
const
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
inputs
,
std
::
vector
<
PaddleTensor
>
*
outputs
)
{
std
::
vector
<
PaddleTensor
>
*
outputs
,
bool
use_analysis
=
true
)
{
int
batch_size
=
FLAGS_batch_size
;
int
num_times
=
FLAGS_repeat
;
auto
predictor
=
CreatePaddlePredictor
<
AnalysisConfig
,
PaddleEngineKind
::
kAnalysis
>
(
config
);
auto
predictor
=
GetPrediction
(
config
,
use_analysis
);
Timer
timer
;
timer
.
tic
();
for
(
int
i
=
0
;
i
<
num_times
;
i
++
)
{
...
...
@@ -93,7 +131,8 @@ void TestOneThreadPrediction(
void
TestMultiThreadPrediction
(
AnalysisConfig
config
,
const
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
inputs
,
std
::
vector
<
PaddleTensor
>
*
outputs
,
int
num_threads
)
{
std
::
vector
<
PaddleTensor
>
*
outputs
,
int
num_threads
,
bool
use_analysis
=
true
)
{
int
batch_size
=
FLAGS_batch_size
;
int
num_times
=
FLAGS_repeat
;
std
::
vector
<
std
::
thread
>
threads
;
...
...
@@ -101,9 +140,7 @@ void TestMultiThreadPrediction(
// TODO(yanchunwei): Bug here, the analyzer phase can't be parallelled
// because AttentionLSTM's hard code nodeid will be damanged.
for
(
int
tid
=
0
;
tid
<
num_threads
;
++
tid
)
{
predictors
.
emplace_back
(
CreatePaddlePredictor
<
AnalysisConfig
,
PaddleEngineKind
::
kAnalysis
>
(
config
));
predictors
.
emplace_back
(
GetPrediction
(
config
,
use_analysis
));
}
for
(
int
tid
=
0
;
tid
<
num_threads
;
++
tid
)
{
threads
.
emplace_back
([
&
,
tid
]()
{
...
...
@@ -129,13 +166,25 @@ void TestMultiThreadPrediction(
void
TestPrediction
(
AnalysisConfig
config
,
const
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
inputs
,
std
::
vector
<
PaddleTensor
>
*
outputs
,
int
num_threads
)
{
std
::
vector
<
PaddleTensor
>
*
outputs
,
int
num_threads
,
bool
use_analysis
=
FLAGS_use_analysis
)
{
LOG
(
INFO
)
<<
"use_analysis: "
<<
use_analysis
;
if
(
num_threads
==
1
)
{
TestOneThreadPrediction
(
config
,
inputs
,
outputs
);
TestOneThreadPrediction
(
config
,
inputs
,
outputs
,
use_analysis
);
}
else
{
TestMultiThreadPrediction
(
config
,
inputs
,
outputs
,
num_threads
);
TestMultiThreadPrediction
(
config
,
inputs
,
outputs
,
num_threads
,
use_analysis
);
}
}
void
CompareNativeAndAnalysis
(
AnalysisConfig
config
,
const
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
inputs
)
{
std
::
vector
<
PaddleTensor
>
native_outputs
,
analysis_outputs
;
TestOneThreadPrediction
(
config
,
inputs
,
&
native_outputs
,
false
);
TestOneThreadPrediction
(
config
,
inputs
,
&
analysis_outputs
,
true
);
CompareResult
(
analysis_outputs
,
native_outputs
);
}
}
// namespace inference
}
// namespace paddle
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